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[...]One bank even predicted that 50-70 per cent of jobs could be replaced.
Yet not only is there little consensus on how AI should be used in banking, many of the current efforts to apply machine learning are modest. Rather than racing towards an AI-enabled future, the industry is feeling its way forward. [...]
As a group, banks all agree that AI is important, but their strategies for using it vary wildly. One European bank that took part in the survey told the FT it had 500-800 people working on AI; Nordea, of Sweden, generally believed to be one of the world’s most technologically advanced banks, says it has just 25. AI budgets vary from below $3m to $15m among the few of the 30 big banks approached by the FT who were willing to disclose the data. One bank says it is ramping up spending from less than $3m a year to $50m-plus.
Overall, while banks are experimenting with AI across their businesses, they are not as bullish as public proclamations would suggest. Of the seven big banks willing to estimate the long-term cost savings of AI, six said it would cut costs by less than 20 per cent, others were more optimistic. [...]
Unrealistic expectations are not the only hurdle banks face as they delve deeper into the AI world that promised so much. Several experts say there is a danger that too much investment flows into “sexy” areas such as chatbots at the expense of investment in behind-the-scenes processes where banks could make more significant gains.
So, against all this noise, where are banks focusing their AI attentions? The answer depends in part on what banks consider AI to be. Those that participated in the FT’s research gave definitions as narrow as only programs that perform more basic functions that involve logical reason, learning and self correction without being explicitly programmed (RBC), to a vision of AI that includes all automation (Nomura). One of the definitions was 130 words long. One involved diagrams. [...]
Risk management is a consistent theme that comes up among the banks. Here the science is on their side, as it is for other rote tasks where human intervention can hinder rather than help. “For repetitive tasks without variability (in middle office, in back end) for clearing/settlement/operational processes that are not particularly in need of smarts, then AI approaches are great,” says Pascal Bouvier, a venture partner at Santander InnoVentures, a fintech venture capital fund of the Spanish bank that invests in early stage fintechs including those focused on AI. [...]
As well as risk management applications, JPMorgan’s use of AI to execute trades more efficiently could fall into this category, as could Citi’s development of machine learning to handle pricing requests sent to traders. [...]
Full article on Financial Times (subscription required)